Epilepsy is one of the most frequent neurological disorders. In a significant number of cases, skilled professionals carry out the detection of the epileptic seizures manually. This necessitates automated epileptic seizure detection. Many researchers have presented computational methods for detecting epileptic seizures based on electroencephalogram signals. In this article, we propose a novel and efficient algorithm for detecting the presence of epileptic seizures in heart rate variability (HRV). This algorithm includes feature extraction and classification. Ten features include time and frequency domain analysis and nonlinear features extracted from one-lead electrocardiogram signal of epileptic patients. Extracted features were used as the input of an artificial neural network, which provides the final classification of the HRV segments (existence of epileptic seizure or not). Multilayer perceptron neural networks with different number of hidden layers and five training algorithms were designed. The results show sensitivity, specificity and accuracy of 88.66%, 90% and 88.33%, respectively, in secondary generalised and 83.33%, 86.11% and 84.72%, respectively, in complex partial seizures. The experimental results portray that the proposed algorithm efficiently detects the presence of epileptic seizure in HRV signals and showed a reasonable accuracy in detection.